Optimal Bidding Strategies for Online Ad Auctions with Overlapping Targeting Criteria
Pages 109 - 110
Abstract
We analyze the problem of how to optimally bid for ad spaces in online ad auctions. For this we consider the general case of multiple ad campaigns with overlapping targeting criteria. In our analysis we first characterize the structure of an optimal bidding strategy. In particular, we show that an optimal bidding strategies decomposes the problem into disjoint sets of campaigns and targeting groups. In addition, we show that pure bidding strategies that use only a single bid value for each campaign are not optimal when the supply curves are not continuous. For this case, we derive a lower-bound on the optimal cost of any bidding strategy, as well as mixed bidding strategies that either achieve the lower-bound, or can get arbitrarily close to it.
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References
[1]
R. Gummadi, P. B. Key, and A. Proutiere. 2011. Optimal bidding strategies in dynamic auctions with budget constraints. In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton). 588--588. https://doi.org/10.1109/Allerton.2011.6120220
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Chong Jiang, Carolyn L. Beck, and R Srikant. 2014. Bidding with limited statistical knowledge in online auctions. ACM SIGMETRICS Performance Evaluation Review, Vol. 41 (04 2014), 38--41. https://doi.org/10.1145/2627534.2627546
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Weinan Zhang and Jun Wang. 2015. Statistical Arbitrage Mining for Display Advertising. (06 2015). https://doi.org/10.1145/2783258.2783269
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Weinan Zhang, Shuai Yuan, and Jun Wang. 2014. Optimal Real-time Bidding for Display Advertising. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14). ACM, New York, NY, USA, 1077--1086. https://doi.org/10.1145/2623330.2623633
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Weinan Zhang, Ying Zhang, Bin Gao, Yong Yu, Xiaojie Yuan, and Tie-Yan Liu. 2012. Joint optimization of bid and budget allocation in sponsored search. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (08 2012). https://doi.org/10.1145/2339530.2339716
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- Optimal Bidding Strategies for Online Ad Auctions with Overlapping Targeting Criteria
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Optimal Bidding Strategies for Online Ad Auctions with Overlapping Targeting Criteria
SIGMETRICSWe analyze the problem of how to optimally bid for ad spaces in online ad auctions. For this we consider the general case of multiple ad campaigns with overlapping targeting criteria. In our analysis we first characterize the structure of an optimal ...
Optimal Bidding Strategies for Online Ad Auctions with Overlapping Targeting Criteria
We analyze the problem of how to optimally bid for ad spaces in online ad auctions. For this we consider the general case of multiple ad campaigns with overlapping targeting criteria. In our analysis we first characterize the structure of an optimal ...
Overlapping online auctions: empirical characterization of bidder strategies and auction prices
Online auctions enable market-level interactions or interdependency of outcomes, which were not observed in physical auctions. One such set of interactions takes place when multiple auctions are conducted to sell identical items by an identical seller ...
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Information & Contributors
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Published In
June 2020
124 pages
ISBN:9781450379854
DOI:10.1145/3393691
- General Chair:
- Edmund Yeh,
- Program Chairs:
- Athina Markopoulou,
- Y.C. Tay
Copyright © 2020 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 June 2020
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- Ontario Centres of Excellence (OCE)
- Natural Sciences and Engineering Research Council of Canada (NSERC)
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SIGMETRICS '20
Sponsor:
SIGMETRICS '20: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems
June 8 - 12, 2020
MA, Boston, USA
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Overall Acceptance Rate 459 of 2,691 submissions, 17%
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